To train the question generation model, we need focal points paired with the questions that they led to generate. However, most question generation datasets have questions paired with answers instead of focal points. Therefore, we use cosine similarity to match the extracted focal points \(f_i \in F\)  with the gold answers during training. I.e.  we calculate \(sim(emb(f_i),emb(a_j))\) : the cosine similarity between the embeddings of \(f_i\)  and the gold answer \(a_j\) .
Following this, we greedily match each answer (and associated question) with the highest scoring focal point. We then remove the latter from the set of focal points available for matching.
In our experiments, we generate the embeddings \(emb(f_i)\)  and \(emb(a_j)\)  using Sentence-BERT [1]}.
